Underreaching. GNNs usually use a fixed number of layers, which only allows them to aggregate information from a few hops away. This can cause a problem called underreaching [1]}, when the important information is not reached. Historically, training GNNs with more than a couple of layers proved to be quite difficult due to oversmoothing [2]} and oversquashing [1]}. To address this, many deeper GNN architectures have been proposed that use various normalization techniques, skip connections, adaptive information propagation, and weight tying [4]}, [5]}, [6]}. While they allow for hundreds of layers, this normally comes at the expense of long training and evaluation times.
